watson machine learning api

This article is translated from awesome-machine-learning by bole online-toolate. Welcome to the technical translation team. For more information, see the requirements at the end of the article. This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning fi

of the Maximum entropy classifier. 16. io is a Retina API with fast and accurate natural language processing algorithms similar to the brain. 17. JSAT is a quick start machine learning library. This library was developed in my spare time and released based on GPL3. Some content in the library can be learned independently. for example, all codes are independent.

This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning field ).C ++ Computer Vision CCV-Machine Vision Library Based on C Language/provided Cache/core, novel machine vision Library Opencv-it provides C ++, C, Python, Java and Matlab interfaces, and

Java that allows rapid development of machine learning and statistical applications. The core focus of the framework is a large number of machine learning algorithms and statistical testing that can handle medium-sized datasets.9. Deeplearning4j is the first commercially-available, open-source, distributed in-depth

applications. The core focus of the framework includes a large number of machine learning algorithms as well as statistical testing that can handle a medium-scale dataset. 9. Deeplearning4j is the first commercial-level, open source, distributed in-depth learning library written using Java and Scala. The purpose of the design is to be used in a business environm

Using Python3 to learn the use of the support vector machine API in Sklearncan come to my git download source code:https://github.com/linyi0604/kaggle1 #Import the Handwriting font loader2 fromSklearn.datasetsImportload_digits3 fromSklearn.cross_validationImportTrain_test_split4 fromSklearn.preprocessingImportStandardscaler5 fromSklearn.svmImportlinearsvc6 fromSklearn.metricsImportClassification_report

computing tools. So we excluded Scipy (although we also use it !). Another thing that needs to be mentioned is that we will also evaluate these libraries based on the integration results with other scientific computing libraries, because Machine Learning (supervised or unsupervised) it is also part of the data processing system. If the database you use does not match other databases in the data processing

data from the webpage, whether through the website API or the webpage capture module beauul ul Soap. Data can be collected through web crawling and applied to machine learning algorithms. 4. In the last step, you must Learn machine learning tools, such as Scikit-Learn, or e

was originally developed by Stanford University, and then Convnetjs began to pop up on GitHub, and the community added many features and tutorials to it. Convnetjs runs directly in the browser environment, supports a variety of learning techniques, and it approaches the underlying principle to make it more suitable for people with experience in neural networks.7. Thing TranslatorThing Translator is a network experiment that allows your phone to recog

libraries are being designed and written, and are slowly being widely accepted and adopted by research circles and industry. These models and libraries include: NLTK packages for natural language processing, word vector technology Word2vec, xgboost models that provide powerful predictive capabilities, and TensorFlow frameworks that Google publishes for Deep learning, and so on. More exciting is that these most popular libraries and models, not only p

solutions on personal computers are easiest to master, while large-scale applications require larger scale and hosted-dependent solutions. Google's cloud machine learning goal is to support a full-area solution and provide a seamless transition from on-premises to cloud environments. theCloud Machine Learningoffering allows users to run custom distributed

supervised and unsupervised learning, and stepping into core technologies such as classification, regression, clustering, and dimensionality reduction, and then explaining the more commonly used and classic algorithms, as well as advanced content such as feature selection and model validation. After completing this tutorial, participants will have a clearer understanding of the machine

Connect Azure machine Learning (iii) to create an Azure machine learning experiment, the next step is to really publish the predictive model of Azure machine learning as a Web service. To enable the Web Service publishing task, fi

Python Chinese translation-nltk supporting book;2. "Python Text processing with NLTK 2.0 Cookbook", this book to go deeper, will involve NLTK code structure, but also will show how to customize their own corpus and model, etc., quite good Pattern The pattern, produced by the clips Laboratory at the University of Antwerp in Belgium, objectively says that pattern is not just a set of text processing tools, it is a Web data mining tool that includes data capture modules (includi

The upcoming Apache Spark 2.0 will provide a machine learning model persistence capability. The persistence of machine learning models (the preservation and loading of machine learning models) makes the following three types of

. 1.5 Steps to develop a machine learning applicationThis book learns and uses machine learning algorithms to develop applications that typically follow the steps below.(1) Collect data. We can use many methods to collect sample data, such as: the production of web crawlers from the site to extract data from the

cost.TensorFlow Estimator API TensorFlow is made up of several parts, the most common of which is the core API, which provides users with a low-level API to define and train any machine learning algorithms that use symbolic operations. This is also the core function of Ten

body value. HttpContextBase context = (httpcontextbase) request.properties["Ms_httpcontext"];//get traditional context Httprequestbase Request = context. request;//defines the traditional request object string name = Request. Form.keys[0]; if (name = = null) { name = Request. FORM[0]; }In addition, the method of post can not directly in the browser to hit the address to get data, debugging is not very convenient, s

1. What is MlbaseMlbase is part of the spark ecosystem and focuses on machine learning with three components: MLlib, MLI, ML Optimizer. ml optimizer:this layer aims to automating the task of ML pipeline construction. The optimizer solves a search problem over feature extractors and ML algorithms included Inmli and MLlib. The ML Optimizer is currently under active development. Mli:an experime

drag-and-drop machine learning love and hatePosted on March 27, 2017 by Lili Article directory [hide] 1. Past Life 2. Love 3. Hate 4. Summarize Drag-and-drop machine learning is a problem I've been thinking about for a long time. 1. Past Life Drag-and-drop machine